Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry and atomic physics. In this paper, an improved differential evolution algorithm, referred to as emph{msMS}_DE, is proposed to search robust fields for various quantum control problems. In emph{msMS}_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the emph{msMS}_DE algorithm is applied to the control problems of (i) open inhomogeneous quantum ensembles and (ii) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, emph{msMS}_DE is experimentally implemented on femtosecond laser control applications to optimize two-photon absorption and control fragmentation of the molecule $text{CH}_2text{BrI}$. Experimental results demonstrate excellent performance of emph{msMS}_DE in searching for effective femtosecond laser pulses for various tasks.
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